{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Autoencoder\n",
    "\n",
    "Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data', validation_size=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4631f1a4e0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f463c6bbac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = mnist.train.images[2]\n",
    "plt.imshow(img.reshape((28, 28)), cmap='Greys_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Architecture\n",
    "\n",
    "The encoder part of the network will be a typical convolutional pyramid. Each convolutional layer will be followed by a max-pooling layer to reduce the dimensions of the layers. The decoder though might be something new to you. The decoder needs to convert from a narrow representation to a wide reconstructed image. For example, the representation could be a 4x4x8 max-pool layer. This is the output of the encoder, but also the input to the decoder. We want to get a 28x28x1 image out from the decoder so we need to work our way back up from the narrow decoder input layer. A schematic of the network is shown below.\n",
    "\n",
    "<img src='assets/convolutional_autoencoder.png' width=500px>\n",
    "\n",
    "Here our final encoder layer has size 4x4x8 = 128. The original images have size 28x28 = 784, so the encoded vector is roughly 16% the size of the original image. These are just suggested sizes for each of the layers. Feel free to change the depths and sizes, but remember our goal here is to find a small representation of the input data.\n",
    "\n",
    "### What's going on with the decoder\n",
    "\n",
    "Okay, so the decoder has these \"Upsample\" layers that you might not have seen before. First off, I'll discuss a bit what these layers *aren't*. Usually, you'll see **transposed convolution** layers used to increase the width and height of the layers. They work almost exactly the same as convolutional layers, but in reverse. A stride in the input layer results in a larger stride in the transposed convolution layer. For example, if you have a 3x3 kernel, a 3x3 patch in the input layer will be reduced to one unit in a convolutional layer. Comparatively, one unit in the input layer will be expanded to a 3x3 path in a transposed convolution layer. The TensorFlow API provides us with an easy way to create the layers, [`tf.nn.conv2d_transpose`](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose). \n",
    "\n",
    "However, transposed convolution layers can lead to artifacts in the final images, such as checkerboard patterns. This is due to overlap in the kernels which can be avoided by setting the stride and kernel size equal. In [this Distill article](http://distill.pub/2016/deconv-checkerboard/) from Augustus Odena, *et al*, the authors show that these checkerboard artifacts can be avoided by resizing the layers using nearest neighbor or bilinear interpolation (upsampling) followed by a convolutional layer. In TensorFlow, this is easily done with [`tf.image.resize_images`](https://www.tensorflow.org/versions/r1.1/api_docs/python/tf/image/resize_images), followed by a convolution. Be sure to read the Distill article to get a better understanding of deconvolutional layers and why we're using upsampling.\n",
    "\n",
    "> **Exercise:** Build the network shown above. Remember that a convolutional layer with strides of 1 and 'same' padding won't reduce the height and width. That is, if the input is 28x28 and the convolution layer has stride = 1 and 'same' padding, the convolutional layer will also be 28x28. The max-pool layers are used the reduce the width and height. A stride of 2 will reduce the size by a factor of 2. Odena *et al* claim that nearest neighbor interpolation works best for the upsampling, so make sure to include that as a parameter in `tf.image.resize_images` or use [`tf.image.resize_nearest_neighbor`]( `https://www.tensorflow.org/api_docs/python/tf/image/resize_nearest_neighbor). For convolutional layers, use [`tf.layers.conv2d`](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d). For example, you would write `conv1 = tf.layers.conv2d(inputs, 32, (5,5), padding='same', activation=tf.nn.relu)` for a layer with a depth of 32, a 5x5 kernel, stride of (1,1), padding is 'same', and a ReLU activation. Similarly, for the max-pool layers, use [`tf.layers.max_pooling2d`](https://www.tensorflow.org/api_docs/python/tf/layers/max_pooling2d)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\n",
    "targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\n",
    "\n",
    "### Encoder\n",
    "conv1 = tf.layers.conv2d(inputs_, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 28x28x16\n",
    "maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')\n",
    "# Now 14x14x16\n",
    "conv2 = tf.layers.conv2d(maxpool1, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 14x14x8\n",
    "maxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')\n",
    "# Now 7x7x8\n",
    "conv3 = tf.layers.conv2d(maxpool2, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 7x7x8\n",
    "encoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')\n",
    "# Now 4x4x8\n",
    "\n",
    "### Decoder\n",
    "upsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7))\n",
    "# Now 7x7x8\n",
    "conv4 = tf.layers.conv2d(upsample1, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 7x7x8\n",
    "upsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14))\n",
    "# Now 14x14x8\n",
    "conv5 = tf.layers.conv2d(upsample2, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 14x14x8\n",
    "upsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28))\n",
    "# Now 28x28x8\n",
    "conv6 = tf.layers.conv2d(upsample3, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 28x28x16\n",
    "\n",
    "logits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)\n",
    "#Now 28x28x1\n",
    "\n",
    "decoded = tf.nn.sigmoid(logits, name='decoded')\n",
    "\n",
    "loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n",
    "cost = tf.reduce_mean(loss)\n",
    "opt = tf.train.AdamOptimizer(0.001).minimize(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "As before, here wi'll train the network. Instead of flattening the images though, we can pass them in as 28x28x1 arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "epochs = 20\n",
    "batch_size = 200\n",
    "sess.run(tf.global_variables_initializer())\n",
    "for e in range(epochs):\n",
    "    for ii in range(mnist.train.num_examples//batch_size):\n",
    "        batch = mnist.train.next_batch(batch_size)\n",
    "        imgs = batch[0].reshape((-1, 28, 28, 1))\n",
    "        batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: imgs,\n",
    "                                                         targets_: imgs})\n",
    "\n",
    "        print(\"Epoch: {}/{}...\".format(e+1, epochs),\n",
    "              \"Training loss: {:.4f}\".format(batch_cost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yrbfeuhQvueSSoSZ9Lr/llltCzZtvvplfLE3KDb/bd999S3Fub+ae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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46266ffb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n",
    "in_imgs = mnist.test.images[:10]\n",
    "reconstructed = sess.run(decoded, feed_dict={inputs_: in_imgs.reshape((10, 28, 28, 1))})\n",
    "\n",
    "for images, row in zip([in_imgs, reconstructed], axes):\n",
    "    for img, ax in zip(images, row):\n",
    "        ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "\n",
    "fig.tight_layout(pad=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Denoising\n",
    "\n",
    "As I've mentioned before, autoencoders like the ones you've built so far aren't too useful in practive. However, they can be used to denoise images quite successfully just by training the network on noisy images. We can create the noisy images ourselves by adding Gaussian noise to the training images, then clipping the values to be between 0 and 1. We'll use noisy images as input and the original, clean images as targets. Here's an example of the noisy images I generated and the denoised images.\n",
    "\n",
    "![Denoising autoencoder](assets/denoising.png)\n",
    "\n",
    "\n",
    "Since this is a harder problem for the network, we'll want to use deeper convolutional layers here, more feature maps. I suggest something like 32-32-16 for the depths of the convolutional layers in the encoder, and the same depths going backward through the decoder. Otherwise the architecture is the same as before.\n",
    "\n",
    "> **Exercise:** Build the network for the denoising autoencoder. It's the same as before, but with deeper layers. I suggest 32-32-16 for the depths, but you can play with these numbers, or add more layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\n",
    "targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\n",
    "\n",
    "### Encoder\n",
    "conv1 = tf.layers.conv2d(inputs_, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 28x28x32\n",
    "maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')\n",
    "# Now 14x14x32\n",
    "conv2 = tf.layers.conv2d(maxpool1, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 14x14x32\n",
    "maxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')\n",
    "# Now 7x7x32\n",
    "conv3 = tf.layers.conv2d(maxpool2, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 7x7x16\n",
    "encoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')\n",
    "# Now 4x4x16\n",
    "\n",
    "### Decoder\n",
    "upsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7))\n",
    "# Now 7x7x16\n",
    "conv4 = tf.layers.conv2d(upsample1, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 7x7x16\n",
    "upsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14))\n",
    "# Now 14x14x16\n",
    "conv5 = tf.layers.conv2d(upsample2, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 14x14x32\n",
    "upsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28))\n",
    "# Now 28x28x32\n",
    "conv6 = tf.layers.conv2d(upsample3, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 28x28x32\n",
    "\n",
    "logits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)\n",
    "#Now 28x28x1\n",
    "\n",
    "decoded = tf.nn.sigmoid(logits, name='decoded')\n",
    "\n",
    "loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n",
    "cost = tf.reduce_mean(loss)\n",
    "opt = tf.train.AdamOptimizer(0.001).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epochs = 100\n",
    "batch_size = 200\n",
    "# Set's how much noise we're adding to the MNIST images\n",
    "noise_factor = 0.5\n",
    "sess.run(tf.global_variables_initializer())\n",
    "for e in range(epochs):\n",
    "    for ii in range(mnist.train.num_examples//batch_size):\n",
    "        batch = mnist.train.next_batch(batch_size)\n",
    "        # Get images from the batch\n",
    "        imgs = batch[0].reshape((-1, 28, 28, 1))\n",
    "        \n",
    "        # Add random noise to the input images\n",
    "        noisy_imgs = imgs + noise_factor * np.random.randn(*imgs.shape)\n",
    "        # Clip the images to be between 0 and 1\n",
    "        noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n",
    "        \n",
    "        # Noisy images as inputs, original images as targets\n",
    "        batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: noisy_imgs,\n",
    "                                                         targets_: imgs})\n",
    "\n",
    "        print(\"Epoch: {}/{}...\".format(e+1, epochs),\n",
    "              \"Training loss: {:.4f}\".format(batch_cost))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking out the performance\n",
    "\n",
    "Here I'm adding noise to the test images and passing them through the autoencoder. It does a suprising great job of removing the noise, even though it's sometimes difficult to tell what the original number is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mYqIvvvgiiM877zxXwwSLL7/8chAz6RkT2sbKidLQrVs3l0N5IhvTTC6KdQsXLnQ1Q4YM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\n70sL/sbs+bNSpUouh/fRHXfc4WpwbKxbt87VLF2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4624778320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n",
    "in_imgs = mnist.test.images[:10]\n",
    "noisy_imgs = in_imgs + noise_factor * np.random.randn(*in_imgs.shape)\n",
    "noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n",
    "\n",
    "reconstructed = sess.run(decoded, feed_dict={inputs_: noisy_imgs.reshape((10, 28, 28, 1))})\n",
    "\n",
    "for images, row in zip([noisy_imgs, reconstructed], axes):\n",
    "    for img, ax in zip(images, row):\n",
    "        ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "fig.tight_layout(pad=0.1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
